In pattern recognition systems, such as speech recognition, thumb print identification, face recognition and handwriting recognition, Hidden Markov Models (HMMs) have been successfully applied to model the input patterns. Hidden Markov Models provide a series of states for a set of basic tokens that are to be recognized from the input pattern. Under many systems, each state is represented by a mixture model containing a number of distributions, referred to as kernels. For example, in Gaussian mixture models, each state is associated with a plurality of Gaussian distributions. An observation vector is applied to each Gaussian and the resulting probabilities are combined using mixture weights to provide an overall probability of the state.
In such mixture models, the number of kernels assigned to each state is uniform across the states. Thus, the same number of distributions or kernels are trained for each state.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.